Is the SAPS II score valid in surgical intensive care unit patients?

Aims and objectives We investigated the performance of the simplified acute physiology score II (SAPS II) in a large cohort of surgical intensive care unit (ICU) patients and tested the hypothesis that customization of the score would improve the uniformity of fit in subgroups of surgical ICU patients. Methods Retrospective analysis of prospectively collected data from all 12 938 patients admitted to a postoperative ICU between January 2004 and January 2009. Probabilities of hospital death were calculated for original and customized (C1‐SAPS II and C2‐SAPS II) scores. A priori subgroups were defined according to age, probability of death according to the SAPS II score, ICU length of stay (LOS), surgical procedures and type of admission. Results The median ICU LOS was 1 (1–3) day. ICU and hospital mortality rates were 5.8% and 10.3%, respectively. Discrimination of the SAPS II was moderate [area under receiver operating characteristic curve (aROC) = 0.76 (0.75–0.78)], but calibration was poor. This model markedly overestimated hospital mortality rates [standardized mortality rate: 0.35 (0.33–0.37)]. First‐level customization (C1‐SAPS II) did not improve discrimination in the whole cohort or the subgroups, but calibration improved in some subgroups. Second‐level customization (C2‐SAPS II) improved discrimination in the whole cohort [aROC = 0.82 (0.79–0.85)] and most of the subgroups (aROC range 0.65–86). Calibration in this model (C2‐SAPS II) improved in the whole cohort and in subgroups except in patients with ICU LOS 4–14 days and those undergoing neuro‐ or gastrointestinal surgery. Conclusions In this large cohort of surgical ICU patients, performance of the original SAPS II model was generally poor. Although second‐level customization improved discrimination and calibration in the whole cohort and most of the subgroups, it failed to simultaneously improve calibration in the subgroups stratified according to the type of surgery, age or ICU LOS.     

Validation of the SAPS 3 admission prognostic model in patients with cancer in need of intensive care

Objectives To validate the SAPS 3 admission prognostic model in patients with cancer admitted to the intensive care unit (ICU).Design Cohort study.Setting Ten-bed medical–surgical oncologic ICU.Patients and participants Nine hundred and fifty-two consecutive patients admitted over a 3-year period.Interventions None.Measurements and results Data were prospectively collected at admission of ICU. SAPS II and SAPS 3 scores with respective estimated mortality rates were calculated. Discrimination was assessed by area under receiver operating characteristic (AUROC) curves and calibration by Hosmer–Lemeshow goodness-of-fit test. The mean age was 58.3 ± 23.1 years; there were 471 (49%) scheduled surgical, 348 (37%) medical and 133 (14%) emergency surgical patients. ICU and hospital mortality rates were 24.6% and 33.5%, respectively. The mean SAPS 3 and SAPS II scores were 52.3 ± 18.5 points and 35.3 ± 20.7 points, respectively. All prognostic models showed excellent discrimination (AUROC ≥ 0.8). The calibration of SAPS II was poor (p < 0.001). However, the calibration of standard SAPS 3 and its customized equation for Central and South American (CSA) countries were appropriate (p > 0.05). SAPS II and standard SAPS 3 prognostic models tended somewhat to underestimate the observed mortality (SMR > 1). However, when the customized equation was used, the estimated mortality was closer to the observed mortality [SMR = 0.95 (95% CI = 0.84–1.07)]. Similar results were observed when scheduled surgical patients were excluded.Conclusions The SAPS 3 admission prognostic model at ICU admission, in particular its customized equation for CSA, was accurate in our cohort of critically ill patients with cancer.This work was performed at the Intensive Care Unit, Instituto Nacional de Cancer, Rio de Janeiro, Brazil. Financial support: institutional departmental funds. Conflicts of interest: none.     

Customization and external validation of the Simplified Mortality Score for the Intensive Care Unit (SMS-ICU) in Brazilian critically ill patients

PurposeTo customize and externally validate the recently proposed Simplified Mortality Score for the ICU (SMS-ICU, a simple score for 90-day mortality that has no need for ancillary testing results) for in-hospital mortality and to compare its performance to SAPS 3.Material and methodsWe used data from two distinct large cohorts of adult Brazilian patients with unplanned ICU admissions to perform a first-level customization (43,017 patients admitted to 78 ICUs) of the original SMS-ICU score for in-hospital mortality and, sequentially, externally validate it (313,365 patients admitted to 99 ICUs). Performance of SMS-ICU was assessed through measurements of discrimination and calibration and compared with SAPS 3.ResultsIn the validation cohort, median SMS-ICU was 13 (IQR 8–16) points and median SAPS 3 was 44 (IQR 36–51). Discrimination of SMS-ICU was good (AUC 0.817; 95% CI 0.814–0.819) but slightly lower than of SAPS 3 (AUC 0.845; 95% CI 0.843–0.848;). The customized SMS-ICU predictions were comparable to SAPS 3 in terms of calibration.ConclusionIn this external validation of the SMS-ICU in a large Brazilian cohort, we observed good discrimination of SMS-ICU and acceptable calibration after first-level customization. SMS-ICU can be used as a measure of illness severity for acutely admitted ICU patients in clinical studies.     

Predicting six-month mortality of patients with traumatic brain injury: usefulness of common intensive care severity scores

Introduction

The aim of this study was to evaluate the usefulness of the APACHE II (Acute Physiology and Chronic Health Evaluation II), SAPS II (Simplified Acute Physiology Score II) and SOFA (Sequential Organ Failure Assessment) scores compared to simpler models based on age and Glasgow Coma Scale (GCS) in predicting long-term outcome of patients with moderate-to-severe traumatic brain injury (TBI) treated in the intensive care unit (ICU).

Methods

A national ICU database was screened for eligible TBI patients (age over 15 years, GCS 3–13) admitted in 2003–2012. Logistic regression was used for customization of APACHE II, SAPS II and SOFA score-based models for six-month mortality prediction. These models were compared to an adjusted SOFA-based model (including age) and a reference model (age and GCS). Internal validation was performed by a randomized split-sample technique. Prognostic performance was determined by assessing discrimination, calibration and precision.

Results

In total, 1,625 patients were included. The overall six-month mortality was 33%. The APACHE II and SAPS II-based models showed good discrimination (area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.75 to 0.82; and 0.80, 95% CI 0.77 to 0.83, respectively), calibration (P > 0.05) and precision (Brier score 0.166 to 0.167). The SOFA-based model showed poor discrimination (AUC 0.68, 95% CI 0.64 to 0.72) and precision (Brier score 0.201) but good calibration (P > 0.05). The AUC of the SOFA-based model was significantly improved after the insertion of age and GCS (∆AUC +0.11, P < 0.001). The performance of the reference model was comparable to the APACHE II and SAPS II in terms of discrimination (AUC 0.77; compared to APACHE II, ΔAUC −0.02, P = 0.425; compared to SAPS II, ΔAUC −0.03, P = 0.218), calibration (P > 0.05) and precision (Brier score 0.181).

Conclusions

A simple prognostic model, based only on age and GCS, displayed a fairly good prognostic performance in predicting six-month mortality of ICU-treated patients with TBI. The use of the more complex scoring systems APACHE II, SAPS II and SOFA added little to the prognostic performance.     

SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

Objective To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data.Design Prospective multicentre, multinational cohort study.Patients and setting A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002.Measurements and results ICU admission data (recorded within ±1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test =10.56, p=0.39, =14.29, p=0.16). Customised equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit.Conclusions The SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels.Electronic Supplementary Material Electronic supplementary material is included in the online fulltext version of this article and accessible for authorised users:     

The predictive performance of the SAPS II and SAPS 3 scoring systems: A retrospective analysis

PurposeThe purpose was to analyze and compare the performance of Simplified Acute Physiology Score (SAPS) II and SAPS 3 (North Europe Logit) in an intensive care unit (ICU) for internal disorders at a German university hospital.Materials and methodsThis retrospective study was conducted at a single-center 12-bed ICU sector for Internal Medicine in Essen, Germany, within an 18-month period. Data for adult ICU patients (N = 548) were evaluated. SAPS II and SAPS 3 scores were assessed along with the predicted mortality rates. Discrimination was evaluated by calculating the area under the receiver operating characteristic curve, and calibration was evaluated using the Hosmer-Lemeshow goodness-of-fit C-test. The ratios of observed-to-expected deaths (standardized mortality ratio, SMR) were calculated along with the 95% confidence intervals (95% CIs).ResultsThe in-hospital mortality rate was 22.6%, which provided an SMR of 0.91 (95% CI, 0.77-0.99) for SAPS II and 0.62 (95% CI, 0.52-0.71) for SAPS 3. Both SAPS II and SAPS 3 exhibited acceptable discrimination, with an area under the receiver operating characteristic curve of 0.84 (95% CI, 0.79-0.89) and 0.73 (95% CI, 0.67-0.79), respectively. However, SAPS II demonstrated superior SMR-based discrimination, which was closer to the observed mortality rate, compared with SAPS 3. Calibration curves exhibited similar performance based on the Hosmer-Lemeshow goodness-of-fit C-test results: χ² = 7.10 with P = .525 for SAPS II and χ² = 3.10 with P = .876 for SAPS 3. Interestingly, both scores overpredicted mortality.ConclusionsIn this study, SAPS 3 overestimated mortality and therefore appears less suitable for risk evaluation in comparison to SAPS II.     

Customised prediction models based on APACHE II and SAPS II scores in patients with prolonged length of stay in the ICU

OBJECTIVE: To study customized APACHE II and SAPS II models in predicting hospital death in patients with a prolonged length of stay in the ICU. DESIGN: Prospectively collected database. SETTING: Thirteen ICUs with 5-10 beds in Finnish secondary referral hospitals. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was collected between 1994 and 1999 and included 23,953 ICU admissions. In order to customize the original APACHE II and SAPS II models and to validate the models, the database was randomly divided into customization data ( n=12,064) and into validation data ( n=11,889). Logistic regression analysis was used for customization. As the length of the ICU stay was prolonged, the calibration and discrimination of both customized models worsened gradually in the validation data. Patients whose ICU stay lasted 7 days or longer (1,312 patients) consumed more than one half of all ICU days and TISS-points. Among these patients, goodness-of-fit statistics was 221.5 and 306.3 ( P<0.0001 for both) and the areas under ROC curve 0.65 and 0.62 for the customized APACHE and SAPS models, respectively. The models underestimated the risk of death in the low range and overestimated it in the high range of predicted mortality. On the other hand, both models discriminated well between survivors and non-survivors if the ICU stay was 2 days or less. CONCLUSIONS: Despite customization, the predictive models may not support clinical decision-making in those patients who require a high share of resources. More relevant instruments are needed for the prediction of outcome of patient groups who consume the major part of ICU resources.     

Follow-up after intensive care: a single center study

Objectives To study health problems, quality of life, functional status, and memory after intensive care.Setting Adult patients (n=346) discharged from a university hospital ICU.Design and methods Prospective cohort study. Follow-up patients were found using the ICU database and the Peoples Registry. Quality of life (QOL) was measured with the Short Form 36 (SF-36) 6 months after ICU discharge. Semi-structured interviews, questionnaires, Glasgow Outcome Score (recovery), and Karnofsky Index (functional status) were used at consultations 7–8 months after ICU discharge.Results The SF-36 response rate was 64.5%, with scores significantly lower than population scores. Consultation patients (n=136) did not differ from the rest (n=210) regarding age, SAPS II scores, length of stay (LOS), and reasons for ICU admission. At follow-up 67.6% of consultation patients continued most activities, 75% looked after themselves, and 64.7% were non-workers, compared to 40.4% before the ICU admission. During and after the ICU stay, 40% lost more than 10 kg body weight. Fifty-eight (43%) could not remember anything from their ICU stay. At follow-up only 22 (16%) could remember having received information during their ICU stay. Three patients needed referral to other specialities.Conclusions We should focus more on optimizing symptom management and giving repeated information after ICU discharge. Nutritional status and weight loss is another area of concern. More research is needed to find out how the broad range of psychosocial and physical problems following an ICU stay relates to the stay.     

Case-mix-adjusted length of stay and mortality in 23 Finnish ICUs

Objectives  To create a tool for benchmarking intensive care units (ICUs) with respect to case-mix adjusted length of stay (LOS) and to study the association between clinical and economic measures of ICU performance. Design  Observational cohort study. Setting  Twenty-three ICUs in Finland. Patients  A total of 80,854 consecutive ICU admissions during 2000–2005, of which 63,304 met the inclusion criteria. Interventions  None. Measurements and results  Linear regression was used to create a model that predicted ICU LOS. Simplified Acute Physiology Score (SAPS) II, age, disease categories according to Acute Physiology and Chronic Health Evaluation III, single highest Therapeutic Intervention Scoring System score collected during the ICU stay and presence of other ICUs in the hospital were included in the model. Probabilities of hospital death were calculated using SAPS II, age, and disease categories as covariates. In the validation sample, the created model accounted for 28% of variation in ICU LOS across individual admissions and 64% across ICUs. The expected ICU LOS was 2.53 ± 2.24 days and the observed ICU LOS was 3.29 ± 5.37 days, P < 0.001. There was no association between the mean observed − mean expected ICU LOS and standardized mortality ratios of the ICUs (Spearman correlation 0.091, P = 0.680). Conclusions  We developed a tool for the assessment of resource use in a large nationwide ICU database. It seems that there is no association between clinical and economic quality indicators. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Validation of four prognostic scores in patients with cancer admitted to Brazilian intensive care units: results from a prospective multicenter study

Objective

The aim of the present study was to validate the Simplified Acute Physiology Score II (SAPS II) and 3 (SAPS 3), the Mortality Probability Models III (MPM₀-III), and the Cancer Mortality Model (CMM) in patients with cancer admitted to several intensive care units (ICU).

Design

Prospective multicenter cohort study.

Setting

Twenty-eight ICUs in Brazil.

Patients

Seven hundred and seventeen consecutive patients (solid tumors 93%; hematological malignancies 7%) included over a 2-month period.

Interventions

None.

Measurements and main results

Discrimination was assessed by area under receiver operating characteristic (AROC) curves and calibration by Hosmer–Lemeshow goodness-of-fit test. The main reasons for ICU admission were postoperative care (57%), sepsis (15%) and respiratory failure (10%). The ICU and hospital mortality rates were 21 and 30%, respectively. When all 717 patients were evaluated, discrimination was superior for both SAPS II (AROC = 0.84) and SAPS 3 (AROC = 0.84) scores compared to CMM (AROC = 0.79) and MPM₀-III (AROC = 0.71) scores (P < 0.05 in all comparisons). Calibration was better using CMM and the customized equation of SAPS 3 score for South American countries (CSA). MPM₀-III, SAPS II and standard SAPS 3 scores underestimated mortality (standardized mortality ratio, SMR > 1), while CMM tended to overestimation (SMR = 0.48). However, using the SAPS 3 for CSA resulted in more precise estimations of the probability of death [SMR = 1.02 (95% confidence interval = 0.87–1.19)]. Similar results were observed when scheduled surgical patients were excluded.

Conclusions

In this multicenter study, the customized equation of SAPS 3 score for CSA was found to be accurate in predicting outcomes in cancer patients requiring ICU admission.     

Outcome prediction in intensive care: results of a prospective, multicentre, Portuguese study

Objective: To compare the performance of the New Simplified Acute Physiology Score (SAPS II) and Acute Physiology and Chronic Health Evaluation (APACHE) II in an independent database, using formal statistical assessment. Design: Analysis of the database of a multicentre, prospective study. Setting: 19 intensive care units (ICUs) in Portugal. Patients: Data for 1094 patients consecutively admitted to the ICUs were collected over a period of 4 months. Following the original SAPS II and APACHE II criteria, the analysis excluded patients younger than 18 years of age, readmissions, acute myocardial infarction, burns, patients in the post-operative period after coronary artery bypass surgery, and patients with a length of stay in the ICU of less than 24 h. The group analysed comprised 982 patients. Interventions: Collection of the first 24 h admission data necessary for the calculation of SAPS II, APACHE II, Therapeutic Intervention Scoring System (TISS), Simplified TISS, organ system failure and basic demographic statistics. Vital status at discharge from the hospital was registered. Measurements and results: In this cohort, discrimination was better for SAPS II than for APACHE II (SAPS II: area under the receiver operating characteristic curve 0.817, standard error 0.015; APACHE II: 0.787, 0.015; p < 0.001); however, both models presented a poor calibration, with significant differences between observed and predicted mortality (Hosmer-Lemeshow goodness-of-fit tests H and C, p < 0.001). In a stratified analysis, this study was unable to demonstrate any definite pattern of association between the poor performance of the models and specific subgroups of patients except for the most severely ill patients, where both models overestimated mortality. Conclusions: SAPS II performed better than APACHE II in this independent database, but the results do not allow its use, at least without being customised, to analyse quality of care or performance among ICUs in the target population. Received: 2 April 1996 Accepted: 24 October 1996     

Evaluation of the uniformity of fit of general outcome prediction models

Objective: To compare the performance of the New Simplified Acute Physiology Score (SAPS II) and the New Admission Mortality Probability Model (MPM II₀) within relevant subgroups using formal statistical assessment (uniformity of fit). Design: Analysis of the database of a multi-centre, multi-national and prospective cohort study, involving 89 ICUs from 12 European Countries. Setting: Database of EURICUS-I. Patients: Data of 16,060 patients consecutively admitted to the ICUs were collected during a period of 4 months. Following the original SAPS II and MPM II₀ criteria, the following patients were excluded from the analysis: younger than 18 years of age; readmissions; acute myocardial infarction; burn cases; patients in the post-operative period after coronary artery bypass surgery and patients with a length of stay in the ICU shorter than 8 h, resulting in a total of 10,027 cases. Interventions: Data necessary for the calculation of SAPS II and MPM II₀, basic demographic statistics and vital status on hospital discharge were recorded. Formal evaluation of the performance of the models, comprising discrimination (area under ROC curve), calibration (Hosmer-Lemeshow goodness-of-fit H^ and C^ tests) and observed/expected mortality ratios within relevant subgroups. Main results: Better predictive accuracy was achieved in elective surgery patients admitted from the operative room/post-anaesthesia room with gastrointestinal, neurological or trauma diagnoses, and younger patients with non-operative neurological, septic or trauma diagnoses. All these characteristics appear to be linked to a lower severity of illness, with both models overestimating mortality in the more severely ill patients. Conclusions: Concerning the performance of the models, very large differences were apparent in relevant subgroups, varying from excellent to almost random predictive accuracy. These differences can explain some of the difficulties of the models to accurately predict mortality when applied to different populations with distinct patient baseline characteristics. This study stresses the importance of evaluating multiple diverse populations (to generate the design set) and of methods to improve the validation set before extrapolations can be made from the validation setting to new independent populations. It also underlines the necessity of a better definition of the patient baseline characteristics in the samples under analysis and the formal statistical evaluation of the application of the models to specific subgroups. Received: 12 August 1996 Accepted: 22 October 1997     

Oral nystatin prophylaxis of Candida spp. colonization in ventilated critically ill patients

Objective Colonization of multiple body sites is a leading risk factor for Candida spp. infection in intensive care unit (ICU) patients. We evaluated whether oral nystatin prophylaxis reduces Candida spp. colonization in ventilated ICU patients.Design and setting Prospective, randomized, open-label study with blinded assessment of the objective primary evaluation criterion in the medical-surgical ICU of a teaching hospital.Patients The study included 98 consecutive patients mechanically ventilated for at least 48 h (mean age 58±19 years; mean SAPS II 40±11), assigned to either treatment group (n=51) or control group (n=47). Study groups were comparable for age, SAPS II, reason for admission, and immune status.Interventions Patients were randomized to receive oral nystatin (treatment group; 3×10⁶ U per day) or no nystatin (control group). Multiple body sites (trachea, stomach, rectum, urine, groin, and blood) were tested for Candida spp. on admission and then every 3 days by mycologists blinded to group assignment, and the colonization index was determined.Results Colonization by Candida spp. developed in 25% of controls but in none of the treated patients. In multivariate analysis, the absence of nystatin prophylaxis and ICU length of stay were independently associated with Candida spp. colonization. No invasive candidiasis was diagnosed in either study group.Conclusions Oral nystatin prophylaxis efficiently prevented Candida spp. colonization in ICU patients at low risk of developing invasive candidiasis. Further studies are needed to determine whether this strategy remains efficient in reducing Candida spp. infections in higher risk ICU patients.This article is discussed in the editorial available at:     

The ability of the Simplified Acute Physiology Score (SAPS II) to predict outcome in coronary care patients

Objective: To evaluate the applicability of the Simplified Acute Physiology Score (SAPS II) for coronary care patients. Design: Prospective observational cohort study. Setting: Medical ICU of a community teaching hospital. Patients: 1587 consecutive patients admitted over a period of 18 months. Measurements and main results: Patients were divided in two groups according to the primary admission diagnosis: general medical intensive care (ICU) patients and intensive coronary care (CCU) patients. Score prediction was tested using criteria suitable to evaluate the discrimination and calibration properties of SAPS II. Mean SAPS II score was 31.6 (± 20.1) in ICU and 28.3 (± 15.5) in CCU patients (p = 0.06), mean risk of death 0.206 and 0.134 (p = 0.001), and observed hospital mortality 17.8 vs 10.3 %. The area under the receiver operating characteristic curve was 0.888 in ICU and 0.908 in CCU patients (p = 0.5). The correlation between predicted and observed hospital mortality was 0.62 (p = 0.001) in ICU and 0.66 (p = 0.001) in CCU patients. The calibration curves did not differ from each other. The probability of death in survivors and nonsurvivors was equally distributed in ICU and CCU patients (p = 0.5). Conclusion: We conclude that SAPS II is applicable to CCU patients in our unit. Received: 30 October 1996 Accepted: 7 August 1997     

Austrian validation and customization of the SAPS 3 Admission Score

Objective  To test the prognostic performance of the SAPS 3 Admission Score in a regional cohort and to empirically test the need and feasibility of regional customization. Design  Prospective multicenter cohort study. Patients and setting  Data on a total of 2,060 patients consecutively admitted to 22 intensive care units in Austria from October 2, 2006 to February 28, 2007. Measurements and results  The database includes basic variables, SAPS 3, length-of-stay and outcome data. The original SAPS 3 Admission Score overestimated hospital mortality in Austrian intensive care patients through all strata of the severity-of-illness. This was true for both available equations, the General and the Central and Western Europe equation. For this reason a customized country-specific model was developed, using cross-validation techniques. This model showed excellent calibration and discrimination in the whole cohort (Hosmer–Lemeshow goodness-of-fit: Ĥ = 4.50, P = 0.922; Ĉ = 5.61, P = 0.847, aROC, 0.82) as well as in the various tested subgroups. Conclusions  The SAPS 3 Admission Score’s general equation can be seen as a framework for addressing the problem of outcome prediction in the general population of adult ICU patients. For benchmarking purposes, region-specific or country-specific equations seem to be necessary in order to compare ICUs on a similar level. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Philipp G. H. Metnitz, Rui Moreno, Peter Bauer, Barbara Metnitz and Jean-Roger Le Gall are all authors of the main SAPS 3 report [4, 5].     

Evaluation of the SOFA score: a single-center experience of a medical intensive care unit in 303 consecutive patients with predominantly cardiovascular disorders

Objective: To evaluate the use of the Sequential Organ Failure Assessment (SOFA) score, the total maximum SOFA (TMS) score, and a derived variable, the ΔSOFA (TMS score minus total SOFA score on day 1) in medical, cardiovascular patients as a means for describing the incidence and severity of organ dysfunction and the prognostic value regarding outcome. Design: Prospective, clinical study. Setting: Medical intensive care unit in a university hospital. Patients: A total of 303 consecutive patients were included (216 men, 87 women; mean age 62 ± 12.6 years; SAPS II 26.2 ± 12.7). They were evaluated 24 h after admission and thereafter every 24 h until ICU discharge or death between November 1997 and March 1998. Readmissions and patients with an ICU stay shorter than 12 h were excluded. Main outcome measure: Survival status at hospital discharge, incidence of organ dysfunction/failure. Interventions: Collection of clinical and demographic data and raw data for the computation of the SOFA score every 24 h until ICU discharge. Measurements and main results: Length of ICU stay was 3.7 ± 4.7 days. ICU mortality was 8.3 % and hospital mortality 14.5 %. Nonsurvivors had a higher total SOFA score on day 1 (5.9 ± 3.7 vs. 1.9 ± 2.3, p < 0.001) and thereafter until day 8. High SOFA scores for any organ system and increasing number of organ failures (SOFA score ≥ 3) were associated with increased mortality. Cardiovascular and neurological systems (day 1) were related to outcome and cardiovascular and respiratory systems, and admission from another ICU to length of ICU stay. TMS score was higher in nonsurvivors (1.76 ± 2.55 vs. 0.58 ± 1.39, p < 0.01), and ΔSOFA/total SOFA on day 1 was independently related to outcome. The area under the receiver-operating characteristic curve was 0.86 for TMS, 0.82 for SOFA on day 1, and 0.77 for SAPS II. Conclusions: The SOFA, TMS, and ΔSOFA scores provide the clinician with important information on degree and progression of organ dysfunction in medical, cardiovascular patients. On day 1 both SOFA score and TMS score had a better prognostic value than SAPS II score. The model is closely related to outcome and identifies patients who are at increased risk for prolonged ICU stay. Received: 6 August 1999 Final revision received: 3 January 2000 Accepted: 28 March 2000     

The performance of SAPS II in a cohort of patients admitted to 99 Italian ICUs: Results from GiViTI

Objective To assess the validity of SAPS II (new Simplified Acute Physiology Score) in a cohort of patients admitted to a large sample of Italian intensive care units (ICU).Design and setting The ability of the SAPS II scoring system to predict the probability of hospital mortality was assessed with calibration and discrimination measures obtained using published coefficients. A new logistic regression equation was then developed and further formal calibration and discrimination measures were estimated for the customized model.Patients From the 2202 consecutive patients recruited during a 1-month period in 99 ICUs, a total of 1393 patients were included in this validation study.Results When the parameters based on the standard model were applied, the expected probability of mortality did not fit those actually observed in the cohort (p<0.001), although it showed satisfactory discrimination (area under the receiver operating characteristic curve=0.80). Such lack of fit yields an overall under prediction of mortality (observed/expected ratio=1.14) that reflects a uniform pattern across a preselected set of subgroups. Customization allowed new mortality estimates to be calculated, with satisfactory calibration (p=0.82) and a more uniform pattern across subgroups.Conclusions SAPS II maintained its validity in an independent sample of patients recruited in a large network of Italian ICUs only after appropriate adaptation (first-level customization). Whether the determinants of this relatively poor performance are related to differences in unmeasured case-mix, methods of application, or quality of care delivered is a matter for discussion that cannot be solved with the data presently available. However, these findings suggest that caution is warranted before implementing the standard SAPS II scoring system parameters outside formal research projects.GiViTI: Gruppo italiano per la Valutazione degli interventi in Terapia Intensiva (A complete list of study participants appears in Appendix 2)     

Case mix,outcomes and comparison of risk prediction models for admissions to adult,general and specialist critical care units for head injury: a secondary analysis of the ICNARC Case Mix Programme Database

Introduction

This report describes the case mix and outcome (mortality, intensive care unit (ICU) and hospital length of stay) for admissions to ICU for head injury and evaluates the predictive ability of five risk adjustment models.

Methods

A secondary analysis was conducted of data from the Intensive Care National Audit and Research Centre (ICNARC) Case Mix Programme, a high quality clinical database, of 374,594 admissions to 171 adult critical care units across England, Wales and Northern Ireland from 1995 to 2005. The discrimination and calibration of five risk prediction models, SAPS II, MPM II, APACHE II and III and the ICNARC model plus raw Glasgow Coma Score (GCS) were compared.

Results

There were 11,021 admissions following traumatic brain injury identified (3% of all database admissions). Mortality in ICU was 23.5% and in-hospital was 33.5%. Median ICU and hospital lengths of stay were 3.2 and 24 days, respectively, for survivors and 1.6 and 3 days, respectively, for non-survivors. The ICNARC model, SAPS II and MPM II discriminated best between survivors and non-survivors and were better calibrated than raw GCS, APACHE II and III in 5,393 patients eligible for all models.

Conclusion

Traumatic brain injury requiring intensive care has a high mortality rate. Non-survivors have a short length of ICU and hospital stay. APACHE II and III have poorer calibration and discrimination than SAPS II, MPM II and the ICNARC model in traumatic brain injury; however, no model had perfect calibration.

     

Body temperature alterations in the critically ill

Objective To determine the incidence of body temperature (BT) alterations in critically ill patients, and their relationship with infection and outcome.Design Prospective, observational study.Setting Thirty-one bed, medico-surgical department of intensive care.Patients Adult patients admitted consecutively to the ICU for at least 24 h, during 6 summer months.Interventions None.Results Fever (BT38.3°C) occurred in 139 (28.2%) patients and hypothermia (BT36°C) in 45 (9.1%) patients, at some time during the ICU stay. Fever was present in 52 of 100 (52.0%) infected patients without septic shock, and in 24 of 38 (63.2%) patients with septic shock. Hypothermia occurred in 5 of 100 (5.0%) infected patients without septic shock and in 5 of 38 (13.1%) patients with septic shock. Patients with hypothermia and fever had higher Sequential Organ Failure Assessment (SOFA) scores on admission (6.3±3.7 and 6.4±3.3 vs 4.6±3.2; p<0.01), maximum SOFA scores during ICU stay (7.6±5.2 and 8.2±4.7 vs 5.4±3.8; p<0.01) and mortality rates (33.3 and 35.3% vs 10.3%; p<0.01). The length of stay (LOS) was longer in febrile patients than in hypothermic and normothermic (36°C<BT<38.3°C) patients [median 6 (1–57) vs 5 (2–28) and 3 (1–33) days, p=0.02 and p=0.01, respectively). Among the septic patients hypothermic patients were older than febrile patients (69±9 vs 54±7 years, p=0.01). Patients with septic shock had a higher mortality if they were hypothermic than if they were febrile (80 vs 50%, p<0.01).Conclusions Both hypothermia and fever are associated with increased morbidity and mortality rates. Patients with hypothermia have a worse prognosis than those with fever.